Masselink Jana, Lappe Markus
Institute for Psychology and Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany.
PLoS Comput Biol. 2025 Jun 4;21(6):e1013041. doi: 10.1371/journal.pcbi.1013041. eCollection 2025 Jun.
In current computational models on oculomotor learning 'the' movement vector is adapted in response to targeting errors. However, for saccadic eye movements, learning exhibits a spatially distributive nature, i.e. it transfers to surrounding positions. This adaptation field resembles the topographic maps of visual and motor activity in the brain and suggests that learning does not act on the population vector but already on the level of the 2D population response. Here we present a population-based gain field model for saccade adaptation in which sensorimotor transformations are implemented as error-sensitive gain field maps that modulate the population response of visual and motor signals and of the internal saccade estimate based on corollary discharge (CD). We fit the model to saccades and visual target localizations across adaptation, showing that adaptation and its spatial transfer can be explained by locally distributive learning that operates on visual, motor and CD gain field maps. We show that 1) the scaled locality of the adaptation field is explained by population coding, 2) its radial shape is explained by error encoding in polar-angle coordinates, and 3) its asymmetry is explained by an asymmetric shape of learning rates along the amplitude dimension. Learning exhibits the highest peak rate, the widest spatial extension and a pronounced asymmetry in the motor domain, while in the visual and the internal saccade domain learning appears more localized. Moreover, our results suggest that the CD-based internal saccade representation has a response field that monitors only part of the ongoing saccade changes during learning. Our framework opens the door to study spatial generalization and interference of learning in multiple contexts.
在当前关于眼球运动学习的计算模型中，“运动向量”会根据目标误差进行调整。然而，对于扫视眼动，学习呈现出空间分布的特性，即它会转移到周围位置。这种适应场类似于大脑中视觉和运动活动的地形图，表明学习并非作用于总体向量，而是已经在二维总体反应层面上起作用。在此，我们提出一种基于总体的扫视适应增益场模型，其中感觉运动转换被实现为误差敏感的增益场图，该图根据推测放电（CD）来调制视觉和运动信号以及内部扫视估计的总体反应。我们将该模型与适应过程中的扫视和视觉目标定位进行拟合，结果表明适应及其空间转移可以通过在视觉、运动和CD增益场图上进行的局部分布式学习来解释。我们表明：1）适应场的缩放局部性可通过总体编码来解释；2）其径向形状可通过极角坐标中的误差编码来解释；3）其不对称性可通过沿幅度维度的学习率不对称形状来解释。学习在运动域表现出最高的峰值速率、最宽的空间扩展和明显的不对称性，而在视觉和内部扫视域，学习似乎更具局部性。此外，我们的结果表明基于CD的内部扫视表征具有一个反应场，该反应场在学习过程中仅监测正在进行的扫视变化的一部分。我们的框架为研究多种情境下学习的空间泛化和干扰打开了大门。
